import folium
from operator import index
from types import prepare_class
from pandas.core.frame import DataFrame
from sklearn import linear_model
import housing_util as hu
df = hu.load_housing_data()
df
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY |
| 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY |
| 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY |
| 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 | NEAR BAY |
| 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20635 | -121.09 | 39.48 | 25.0 | 1665.0 | 374.0 | 845.0 | 330.0 | 1.5603 | 78100.0 | INLAND |
| 20636 | -121.21 | 39.49 | 18.0 | 697.0 | 150.0 | 356.0 | 114.0 | 2.5568 | 77100.0 | INLAND |
| 20637 | -121.22 | 39.43 | 17.0 | 2254.0 | 485.0 | 1007.0 | 433.0 | 1.7000 | 92300.0 | INLAND |
| 20638 | -121.32 | 39.43 | 18.0 | 1860.0 | 409.0 | 741.0 | 349.0 | 1.8672 | 84700.0 | INLAND |
| 20639 | -121.24 | 39.37 | 16.0 | 2785.0 | 616.0 | 1387.0 | 530.0 | 2.3886 | 89400.0 | INLAND |
20640 rows × 10 columns
import folium
import housing_util as hu
df = hu.load_housing_data()
df
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY |
| 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY |
| 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY |
| 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 | NEAR BAY |
| 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20635 | -121.09 | 39.48 | 25.0 | 1665.0 | 374.0 | 845.0 | 330.0 | 1.5603 | 78100.0 | INLAND |
| 20636 | -121.21 | 39.49 | 18.0 | 697.0 | 150.0 | 356.0 | 114.0 | 2.5568 | 77100.0 | INLAND |
| 20637 | -121.22 | 39.43 | 17.0 | 2254.0 | 485.0 | 1007.0 | 433.0 | 1.7000 | 92300.0 | INLAND |
| 20638 | -121.32 | 39.43 | 18.0 | 1860.0 | 409.0 | 741.0 | 349.0 | 1.8672 | 84700.0 | INLAND |
| 20639 | -121.24 | 39.37 | 16.0 | 2785.0 | 616.0 | 1387.0 | 530.0 | 2.3886 | 89400.0 | INLAND |
20640 rows × 10 columns
def add_circles(point, map, min_pop, max_pop):
# https://leafletjs.com/reference-1.6.0.html#circle
folium.Circle(
radius = (point.population - min_pop)/(max_pop-min_pop)*30,
weight = 1,
opacity = 0.4,
location = [point.latitude, point.longitude],
color="crimson"
).add_to(map)
map = folium.Map(width=600, height=400, zoom_start=2)
# Use df.apply(axis=1) to "iterate" through every row in your dataframe
df.apply(
add_circles,
args=(
map,
min(df.population),
max(df.population)
),
axis=1
)
# Zoom in the plotted points
map.fit_bounds(map.get_bounds())
# Save the map to an HTML file
map.save('html_map_output/housing_scatter.html')
map